1. Field of the Invention
The present invention relates to a technique used for matching between an image acquired by medical imaging equipment and an ROI (Region Of interest) set on the image.
2. Description of the Related Art
A medical image is generally obtained as mapping data having a value for each voxel obtained by dividing the image in the form of a lattice with a given size. If this mapping data is to be managed without any change, the amount of data to be managed becomes enormous, resulting in difficulty in making data correspond to clinical diagnoses. In general, therefore, some kind of information compression technique is used to store data as disease-specific databases and reduce the amount of data. As one of such methods, there is available a method of analyzing, or example, image data obtained by imaging a given organ or the like upon dividing (ROI division) the data into anatomically, physiologically significant spatial areas. Currently, for example, ROI division applied to an artery dominant region of a cerebral blood flow has been reported.
There are many needs and applications for such ROI division in the overall field of medical images, including diagnoses using SPECT and PET.
For example, strokes have recently become the third cause of death, following malignant tumors and cardiovascular diseases. Of the strokes, cerebral infarction occupies 60% of them and keeps increasing. In stroke diagnosis, the arteries of the cerebral blood vessels symmetrically include six main blood vessels, and dominant regions are determined by brain tissue, each of which can be three-dimensionally divided. When blood flows in these blood vessels are to be analyzed, a doctor divides an image of each patient which is represented by data acquired in the time-axis direction by setting a template ROI on each image (image division by ROI setting), and diagnosis information such as a time intensity curve (TIC) is generated for each ROI. In this case, a template ROI is set on an image to divide the area on the image into significant areas in anatomical terms, physiological terms, or other scientific terms. In general, medically standardized images are used. Conventionally, diagnosis target images having differences are warped into anatomically standardized images, and standard template ROIs defined in advance are then applied to the images (e.g., see Friston K J. Spatial registration and normalization of images. Human Brain Mapping. 2, 165-189, (1995), and Yoshitaka Uchida et al., “Statistical Image Diagnosis (3D SSP)”, The Japanese Society of Technology Education, Vol. 58, No. 12, pp. 1563-1572 (2002) for anatomical standardization of images, and see Ryo Takeuchi, “Cerebral Nuclear Medicine Fully Automatic ROI Analysis Program: 3D SRT”, The Japanese Society of Technology Education, Vol. 59, No. 12, pp. 1463-1474 (2003) for ROI setting).
In a conventional image division method based on ROI setting, however, the following problems arise.
First, a problem may be caused by an image warp error. That is, an image warp error may occur due to a warp algorithm for a diagnosis target image, the quality of target data, or the difference (individual difference) between standardized data and a diagnosis target image. This may cause artifacts. Conventional image warp techniques have achieved success to some extent in SPECT or PET. This is because, some limitations are imposed on the spatial resolution of original data, and image warp is executed upon decreasing the spatial resolution so as to reduce errors. For MRI and CT which originally have high spatial resolutions, higher accuracy is required than for SPECT and PET, and hence it is not always adequate to use a conventional technique based on image warp.
Second, an enormous processing cost may be required. That is, according to the conventional technique based on image warp, since a diagnosis target image acquired by imaging is used, spatially nonlinear warp processing is required. This nonlinear warp processing demands a high processing cost, and the cost may further increase depending on the number of data or the amount of data. For example, in the case of MRI, this processing must be applied to all data of several parameter types even for one examination unit, and hence this problem is especially serious.
Third, the image division technique is not suitable for applications which do not require morphological warp of diagnosis target images. That is, the conventional technique based on image warp cannot be applied to applications which do not want to warp the shapes of diagnosis target images, e.g., surgical operation and radiation therapy applications. In addition, it is sometimes necessary to superimpose and display an original image and an ROI, instead of displaying only the numerical value of the ROI. If only warped data is stored, the data needs to be inversely warped. It is, however, technically difficult to perform such inverse warp because, for example, the spatial resolution has already been degraded. Alternatively, diagnosis target images may be separately stored to be referred to as needed. Such an arrangement, however, is not practical.